awesome-flake8-extensions
fastapi
awesome-flake8-extensions | fastapi | |
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4 | 469 | |
1,193 | 71,023 | |
- | - | |
6.4 | 9.8 | |
about 1 month ago | 8 days ago | |
Python | ||
GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-flake8-extensions
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
Ultimately we want to test our code with Flake8 and plugins to enforce a more consistent code style and to encourage best practices. When you first introduce flake8 or a new plug-in commonly you have a lot of violations that you can silence with a #noqa comment. When you first introduce a new flake8 plugin, you will likely have a lot of violations, which you silence with #noqa comments. Over time these comments will become obsolete because you fixed the. yesqa will automatically remove these unnecessary #noqa comments.
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Python toolkits
flake8 for linting along with following plugin (list of awesome plugin can be found here, but me and my teammates have selected the below one. Have linting but don't make it too hard.) flake8-black which uses black for code formatting check. flake8-isort which uses isort for separation of import in section and formatting them alphabetically. flake8-bandit which uses bandit for security linting. flake8-bugbear for finding likely bugs and design problems in your program. flake8-bugbear - Finding likely bugs and design problems in your program. pep8-naming for checking the PEP-8 naming conventions. mccabe for Ned’s script to check McCabe complexity flake8-comprehensions for writing better list/set/dict comprehensions.
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Write better Python - with some help!
In addition to this out of the box -linting, there are loads of flake8 extensions that can help you with for example switching from .format() to using f-strings or checking that your naming follows the PEP8 guidelines. For example, adding flake8-length adds line length checking to the linting.
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Standards to be aware of
And if you're using flake8, make sure to check out its plugins. Here's a good list: https://github.com/DmytroLitvinov/awesome-flake8-extensions
fastapi
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Github Sponsor Sebastián Ramírez Python programmer
He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.
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Python: A SQLAlchemy Wrapper Component That Works With Both Flask and FastAPI Frameworks
It has been an interesting exercise developing this wrapper component. The fact that it seamlessly integrates with the FastAPI framework is just a bonus for me; I didn't plan for it since I hadn't learned FastAPI at the time. I hope you find this post useful. Thank you for reading, and stay safe as always.
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FastAPI Best Practices: A Condensed Guide with Examples
FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.
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Building an Email Assistant Application with Burr
In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.
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FastAPI Got Me an OpenAPI Spec Really... Fast
That’s when I found FastAPI.
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How to Deploy a Fast API Application to a Kubernetes Cluster using Podman and Minikube
FastAPI & Uvicorn
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Analysing FastAPI Middleware Performance
Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696
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LangChain, Python, and Heroku
An API application framework (such as FastAPI)
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Litestar – powerful, flexible, and highly performant Python ASGI framework
It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.
This ends up made worse if you’re using sync routes.
If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.
This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759
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AI-Powered Image Search with CLIP, pgvector, and Fast API
Fast API.
What are some alternatives?
black - The uncompromising Python code formatter
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
HS-Sanic - Async Python 3.6+ web server/framework | Build fast. Run fast. [Moved to: https://github.com/sanic-org/sanic]
unimport - :rocket: The ultimate linter and formatter for removing unused import statements in your code. [Moved to: https://github.com/hakancelikdev/unimport]
Tornado - Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.
pep8-naming - Naming Convention checker for Python
django-ninja - 💨 Fast, Async-ready, Openapi, type hints based framework for building APIs
pyre-check - Performant type-checking for python.
Flask - The Python micro framework for building web applications.
flakes - list of flake8 plugins and their codes
swagger-ui - Swagger UI is a collection of HTML, JavaScript, and CSS assets that dynamically generate beautiful documentation from a Swagger-compliant API.